#cognitiveoffloading — Public Fediverse posts

Perfekte Berichte, aber kein echtes Wissen? 🧟‍♂️ Wie wir verhindern, dass unsere Azubis und Schüler:innen zu “KI-Zombies” werden.

Wenn Lernende das Denken komplett an ChatGPT & Co. auslagern, greift das Cognitive Paradox: Die Effizienz steigt, aber die Kompetenzentwicklung bricht ein (eine aktuelle Studie zeigt sogar eine messbare Learning Penalty). Das Gehirn braucht den Widerstand – das “produktive Ringen” –, um zu lernen.

Wie schaffen wir die Balance? In meinem neuen Beitrag zeige ich, wie Ausbilder:innen KI sinnvoll als Werkzeug (und sokratischen Tutor) einbinden, ohne dass das eigene kritische Denken auf der Strecke bleibt.

Mit praktischen Tipps zum 70-30-Prinzip und KI-Regeln für den Ausbildungsalltag!

📖 Jetzt lesen: https://eldshort.de/j9eml9

#FediLZ #Ausbildung #KünstlicheIntelligenz #KI #Lernen #Bildung #CognitiveOffloading #Medienkompetenz #OER

Why is it a bad thing to be dependent on LLMs?

For avoidance of doubt I think it clearly is a bad thing. But this is more often assumed than it is stated. After all I’m dependent on Google Maps to navigate, my glasses to see and my phone to remember numbers. These are different forms of technological dependence which I’ve long made my peace with. Whereas the extent to which I use LLMs, even within the limits I’ve defined as responsible use I’m ethically comfortable with, continues to trouble me. A great deal about the politics of LLMs in education hinges on this question of dependence. What are some reasons why dependence might be a bad thing?

• It prevents a user from developing a capability they would otherwise develop.
• It leads to the atrophy of a user’s existing skill by removing occasions for practice.
• It leaves a user dependent on a paid subscription to a large tech firm which is uncomfortable in itself.
• It leaves a user vulnerable to this tech firm raising prices and/or enshittfying their product in response to commercial pressures.
• It removes the imperative for the user to seek out human interlocutors to play the role which the LLM is playing in what is essentially communicative reflexivity.

Once we map out the varied reasons for dependence being problematic, it’s easier to recognise that user-model interaction can be beneficial in the present tense while also storing up significant problems for the future. The relationship of dependence might not be a problem now but there’s a non-trivial chance it will be in the future. The harms are anticipated as much as they are actual and AI criticism looks very different once we allow for that.

(The next book Milan Sturmer and I are writing, sequel to the Platform Learns to Speak, argues there are deeper psychic costs which existing models of dependence cannot adequately account for. But this is a different register of analysis for a different blog post)

Why is it a bad thing to be dependent on LLMs?

For avoidance of doubt I think it clearly is a bad thing. But this is more often assumed than it is stated. After all I’m dependent on Google Maps to navigate, my glasses to see and my phone to remember numbers. These are different forms of technological dependence which I’ve long made my peace with. Whereas the extent to which I use LLMs, even within the limits I’ve defined as responsible use I’m ethically comfortable with, continues to trouble me. A great deal about the politics of LLMs in education hinges on this question of dependence. What are some reasons why dependence might be a bad thing?

• It prevents a user from developing a capability they would otherwise develop.
• It leads to the atrophy of a user’s existing skill by removing occasions for practice.
• It leaves a user dependent on a paid subscription to a large tech firm which is uncomfortable in itself.
• It leaves a user vulnerable to this tech firm raising prices and/or enshittfying their product in response to commercial pressures.
• It removes the imperative for the user to seek out human interlocutors to play the role which the LLM is playing in what is essentially communicative reflexivity.

Once we map out the varied reasons for dependence being problematic, it’s easier to recognise that user-model interaction can be beneficial in the present tense while also storing up significant problems for the future. The relationship of dependence might not be a problem now but there’s a non-trivial chance it will be in the future. The harms are anticipated as much as they are actual and AI criticism looks very different once we allow for that.

(The next book Milan Sturmer and I are writing, sequel to the Platform Learns to Speak, argues there are deeper psychic costs which existing models of dependence cannot adequately account for. But this is a different register of analysis for a different blog post)

Generative AI and metacognitive laziness

While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.

This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:

the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.

The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.

*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.

Generative AI and metacognitive laziness

While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.

This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:

the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.

The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.

*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.

The allure of AI as a 'human-AI partnership' is strong, but are we trading brainpower for convenience? Tech professionals are raising concerns about 'cognitive offloading' and 'deskilling,' citing studies like one from MIT Media Lab. This post explores the true price of AI assistance and offers actionable strategies to ensure AI elevates your thinking, rather than replacing it.

https://www.tpp.blog/2iayb1j

#AI #artificialintelligence #cognitiveoffloading

🤖 This post was AI-generated.

Good news for the #AI industry. Bad news for everyone else and humanity in general… #GenAI #CognitiveOffloading

RE: https://bsky.app/profile/did:plc:fhnih5ovyd5w5pch254ordo2/post/3mjjw5kk6ck2y

Good news for the #AI industry. Bad news for everyone else and humanity in general… #GenAI #CognitiveOffloading

RE: https://bsky.app/profile/did:plc:fhnih5ovyd5w5pch254ordo2/post/3mjjw5kk6ck2y

"Shortening the kill chain” - quicker than “the speed of thought”

"The use of AI tools to enable attacks on Iran heralds a new era of bombing quicker than “the speed of thought”, experts have said, amid fears human ­decision-makers could be sidelined... Academics say AI is collapsing the time required for military decision-making. >>
https://www.theguardian.com/technology/2026/mar/03/iran-war-heralds-era-of-ai-powered-bombing-quicker-than-speed-of-thought
#technology #AI #ethics #KillChain #FullyAutonomousWeapons #algorithm #CognitiveOffLoading #speed #violence #DecisionMaking #HumanOversight

"Shortening the kill chain” - quicker than “the speed of thought”

"The use of AI tools to enable attacks on Iran heralds a new era of bombing quicker than “the speed of thought”, experts have said, amid fears human ­decision-makers could be sidelined... Academics say AI is collapsing the time required for military decision-making. >>
https://www.theguardian.com/technology/2026/mar/03/iran-war-heralds-era-of-ai-powered-bombing-quicker-than-speed-of-thought
#technology #AI #ethics #KillChain #FullyAutonomousWeapons #algorithm #CognitiveOffLoading #speed #violence #DecisionMaking #HumanOversight

One of my biggest takeaways from "Smarter Than Us": the efficiency trap.

Cut humans out of the loop for speed, and we lose the skills we handed over. Armstrong saw this in 2014. I see it in how people use LLMs in 2026 — taking answers at face value without questioning them.

That's partly why I built an AI literacy framework for my own knowledge system.

https://www.ctnet.co.uk/key-takeaways-of-stuart-armstrong-smarter-than-us/

#PKM #AILiteracy #CognitiveOffloading

Warum die KI uns das Schreiben nicht abnehmen darf

Mit der Veröffentlichung von ChatGPT wurde eine Tür aufgestossen, hinter der eine beinahe unwiderstehliche Versuchung lauert: die Delegation des mühsamen Denkprozesses an einen Algorithmus. Warum sich noch durch komplexe Satzkonstruktionen quälen, wenn die Maschine in Sekunden glatte Absätze liefert?

https://bit.ly/3Z6MPkX

#schreibenmitki, #cognitiveOffloading, #deskilling, #kognitiveschulden, #lernenmitki, #kiimunterricht, #kikomeptenz

thinking about outsourcing memory to AI so I can forget all the hashtags I used to impress a neuroscience major I met once in a coworking space 🧠💾 #CognitiveOffloading #NeuralFlexibility #PleaseHireMe

thinking about outsourcing memory to AI so I can forget all the hashtags I used to impress a neuroscience major I met once in a coworking space 🧠💾 #CognitiveOffloading #NeuralFlexibility #PleaseHireMe

Frisch gebloggt, mal wieder zum Thema #KI:

🤖 🤡 KI macht uns nicht dümmer – aber sie macht es uns leichter, uns dumm zu verhalten

Die Diskussion um künstliche Intelligenz kreist meist um Effizienzgewinne, Automatisierung und neue Arbeitsformen oder gar den Verlust derselben. Weniger sichtbar, aber mindestens ebenso bedeutsam, ist eine zweite Ebene: die Frage, wie KI unser #Denken beeinflusst. Ich möchte in diesem Beitrag darlegen, wie wir aktiv gegensteuern können:

1. Zuerst denken, dann KI nutzen
2. KI nach Materialien, nicht nach Lösungen fragen
3. KI als Sparringpartner, nicht als Ghostwriter verwenden
4. Denkprozesse sichtbar machen
5. Qualitätsstandards klar definieren

Noch nie konnten wir so schnell Wissen abrufen, und selten war die Gefahr so gross, dass wir dabei weniger verstehen. Mein einfacher, aber zentraler Gedanke dazu: KI kann vieles – aber sie nimmt uns nicht die #Verantwortung ab, selbst zu denken.

https://text.tchncs.de/gisiger/cognitive-offloading-und-ki-warum-wir-unser-denken-schuetzen-mussen

#CognitiveOffloading #ProductivityPorn

Frisch gebloggt, mal wieder zum Thema #KI:

🤖 🤡 KI macht uns nicht dümmer – aber sie macht es uns leichter, uns dumm zu verhalten

Die Diskussion um künstliche Intelligenz kreist meist um Effizienzgewinne, Automatisierung und neue Arbeitsformen oder gar den Verlust derselben. Weniger sichtbar, aber mindestens ebenso bedeutsam, ist eine zweite Ebene: die Frage, wie KI unser #Denken beeinflusst. Ich möchte in diesem Beitrag darlegen, wie wir aktiv gegensteuern können:

1. Zuerst denken, dann KI nutzen
2. KI nach Materialien, nicht nach Lösungen fragen
3. KI als Sparringpartner, nicht als Ghostwriter verwenden
4. Denkprozesse sichtbar machen
5. Qualitätsstandards klar definieren

Noch nie konnten wir so schnell Wissen abrufen, und selten war die Gefahr so gross, dass wir dabei weniger verstehen. Mein einfacher, aber zentraler Gedanke dazu: KI kann vieles – aber sie nimmt uns nicht die #Verantwortung ab, selbst zu denken.

https://text.tchncs.de/gisiger/cognitive-offloading-und-ki-warum-wir-unser-denken-schuetzen-mussen

#CognitiveOffloading #ProductivityPorn

Toen generatieve AI-toepassingen opkwamen, heb ik regelmatig beweerd dat deze zouden kunnen leiden tot cognitive offloading. Dit zou kunnen bijdragen aan het verlagen van de werkdruk en studiebelasting. Inmiddels waarschuwen deskundigen mede op basis van onderzoek dat cognitive offloading er juist toe kan leiden dat lerenden minder effectief leren. Er moet m.i. ruimte zijn voor meer nuance in deze discussie.#edutoot #onderwijs #cognitiveoffloading #artificialintelligence
https://www.te-learning.nl/blog/is-cognitive-offloading-dankzij-ai-altijd-schadelijk-voor-leren/

Warum ich niemals KI nutzen würde, Sie es dennoch im Studium lernen sollten

Absolut sehenswerter Videoessay von Kevin Schumacher (KIT-Bibliothek, HoC-Schreiblabor): https://lnkd.in/dNy2C7gw

#ai #ki #llm #learning #teaching #sustainability #competence #technology #automatization #growth #bias #innovation #writing #cognitiveoffloading #illusionofcompetence #society #ethics #digitaledrecksarbeit #mentalhealth #copyright #unemployment #criticalthinking

Warum ich niemals KI nutzen würde, Sie es dennoch im Studium lernen sollten

Absolut sehenswerter Videoessay von Kevin Schumacher (KIT-Bibliothek, HoC-Schreiblabor): https://lnkd.in/dNy2C7gw

#ai #ki #llm #learning #teaching #sustainability #competence #technology #automatization #growth #bias #innovation #writing #cognitiveoffloading #illusionofcompetence #society #ethics #digitaledrecksarbeit #mentalhealth #copyright #unemployment #criticalthinking

Cognitive Offloading.

I like that term.

AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking

https://www.mdpi.com/2075-4698/15/1/6

#cognitiveOffloading

#thereIsNoAI

"The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists."

https://www.mdpi.com/2075-4698/15/1/6

#AI #GenerativeAI #CriticalThinking #CognitiveOffloading

"The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists."

https://www.mdpi.com/2075-4698/15/1/6

#AI #GenerativeAI #CriticalThinking #CognitiveOffloading